ALPR is an image-processing approach that often functions as the core module of “intelligent” transportation infrastructure applications. License plate recognition techniques, such as ALPR, can be employed to identify a vehicle by automatically reading a license plate utilizing image processing and character recognition technologies. A license plate recognition operation can be performed by locating a license plate in an image, segmenting the characters in the captured image of the plate, and performing an OCR (Optical Character Recognition) operation with respect to the characters identified.
The ALPR problem is often decomposed into a sequence of image processing operations locating the sub-image containing the license plate (i.e., plate localization), extracting images of individual characters (i.e., segmentation), and performing optical character recognition (OCR) on these character images.
Detection of license plates within a larger vehicle image is a critical step in an automated license plate recognition process. Here it is important to ensure that the true positive rate is extremely high, we don't want to miss plates since these typically translate into revenue (e.g., for tolling applications). However, at the same time the false alarm rate must be kept low since each candidate license plate sub-image will then be subjected to much more intensive processing: segmentation of characters and attempted OCR. A preferred solution for automatic license plate technology uses a SNoW classifier with SMQT features to detect license plates within a captured image. The SNoW classifier is highly accurate and easy to train so it is highly desirable as a classification method. There is very little computation involved in implementing a trained classifier, but the context size of the classifier is large. This results in significant real time to calculate the score used for detection. This limitation reduces the usefulness of the classifier in high speed/bandwidth applications (e.g., heavy traffic).